数据分析和信息处理(英文)最新文献_第3页

GeoAI Technologies and Their Application Areas in Urban Planning and Development: Concepts, Opportunities and Challenges in Smart City (Kuwait, Study Case) GeoAI技术及其在城市规划和发展中的应用领域:智慧城市的概念、机遇和挑战(科威特，研究案例)

数据分析和信息处理(英文) Pub Date : 2022-01-01 DOI: 10.4236/jdaip.2022.102007

Abdelkhalek I. Alastal, Ashraf Hassan Shaqfa

{"title":"GeoAI Technologies and Their Application Areas in Urban Planning and Development: Concepts, Opportunities and Challenges in Smart City (Kuwait, Study Case)","authors":"Abdelkhalek I. Alastal, Ashraf Hassan Shaqfa","doi":"10.4236/jdaip.2022.102007","DOIUrl":"https://doi.org/10.4236/jdaip.2022.102007","url":null,"abstract":"Artificial intelligence has significantly altered many job workflows, hence ex-panding earlier notions of limitations, outcomes, size, and prices. GeoAI is a multidisciplinary field that encompasses computer science, engineering, sta-tistics, and spatial science. Because this subject focuses on real-world issues, it has a significant impact on society and the economy. A broad context incor-porating fundamental questions of theory, epistemology, and the scientific method is used to bring artificial intelligence (Al) and geography together. This connection has the potential to have far-reaching implications for the geographic study. GeoAI, or the combination of geography with artificial intelligence, offers unique solutions to a variety of smart city issues. This paper provides an overview of GeoAI technology, including the definition of GeoAI and the differences between GeoAI and traditional AI. Key steps to successful geographic data analysis include integrating AI with GIS and using GeoAI tools and technologies. Also shown are key areas of applications and models in GeoAI, likewise challenges to adopt GeoAI methods and technology as well as benefits. This article also included a case study on the use of GeoAI in Kuwait, as well as a number of recommendations.","PeriodicalId":71434,"journal":{"name":"数据分析和信息处理(英文)","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70997839","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

Classification of Oil-Gas-Water Three-Phase Flow in a Pipeline Based on BP Neural Network Analysis 基于BP神经网络的管道油气水三相流分类

数据分析和信息处理(英文) Pub Date : 2022-01-01 DOI: 10.4236/jdaip.2022.104012

W. Lu, Peng Li, Xuhui Zhang

引用次数: 0

Fast Object Extraction and Euler Number on Block Represented Images 块表示图像的快速目标提取和欧拉数

数据分析和信息处理(英文) Pub Date : 2022-01-01 DOI: 10.4236/jdaip.2022.102006

Iraklis M. Spiliotis, Alexandros S. Peppas, Nikolaos D. Karampasis, Y. Boutalis

引用次数: 0

A Machine Learning Approach: Enhancing the Predictive Performance of Pharmaceutical Stock Price Movement during COVID 一种机器学习方法:增强COVID期间医药股价格走势的预测性能

数据分析和信息处理(英文) Pub Date : 2022-01-01 DOI: 10.4236/jdaip.2022.101001

Beilei He, Weiyi Han, Suet Ying Isabelle Hon

{"title":"A Machine Learning Approach: Enhancing the Predictive Performance of Pharmaceutical Stock Price Movement during COVID","authors":"Beilei He, Weiyi Han, Suet Ying Isabelle Hon","doi":"10.4236/jdaip.2022.101001","DOIUrl":"https://doi.org/10.4236/jdaip.2022.101001","url":null,"abstract":"Predicting stock price movement direction is a challenging problem influenced by different factors and capricious events. The conventional stock price prediction machine learning models heavily rely on the internal financial features, especially the stock price history. However, there are many outside-of-com-pany features that deeply interact with the companies’ stock price performance, especially during the COVID period. In this study, we selected 9 COVID vaccine companies and collected their relevant features over the past 20 months. We add-ed handcrafted external information, including COVID-related statistics and company-specific vaccine progress information. We implemented, evaluated, and compared several machine learning models, including Multilayer Perceptron Neural Networks with logistic regression and decision trees with boosting and bagging algorithms. The results suggest that the application of feature engineering and data mining techniques can effectively enhance the performance of models predicting stock price movement during the COVID period. The results show that COVID-related handcrafted features help to increase the model prediction accuracy by 7.3% and AUROC by 6.5% on average. Further exploration showed that with data selection the decision tree model with gradient, boosting algorithm achieved 70% in AUROC and 66% in the accuracy.","PeriodicalId":71434,"journal":{"name":"数据分析和信息处理(英文)","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70997867","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Analyzing Differences between Online Learner Groups during the COVID-19 Pandemic through K-Prototype Clustering 基于k -原型聚类分析COVID-19大流行期间在线学习群体差异

数据分析和信息处理(英文) Pub Date : 2022-01-01 DOI: 10.4236/jdaip.2022.101002

Guanggong Ge, Quanlong Guan, Lusheng Wu, Weiqi Luo, Xingyu Zhu

{"title":"Analyzing Differences between Online Learner Groups during the COVID-19 Pandemic through K-Prototype Clustering","authors":"Guanggong Ge, Quanlong Guan, Lusheng Wu, Weiqi Luo, Xingyu Zhu","doi":"10.4236/jdaip.2022.101002","DOIUrl":"https://doi.org/10.4236/jdaip.2022.101002","url":null,"abstract":"Online learning is a very important means of study, and has been adopted in many countries worldwide. However, only recently are researchers able to collect and analyze massive online learning datasets due to the COVID-19 epidemic. In this article, we analyze the difference between online learner groups by using an unsupervised machine learning technique, i.e., k-prototypes clustering. Specifically, we use a questionnaire designed by domain experts to collect various online learning data, and investigate students’ online learning behavior and learning outcomes through analyzing the collected questionnaire data. Our analysis results suggest that students with better learning media generally have better online learning behavior and learning results than those with poor online learning media. In addition, both in economically developed or undeveloped regions, the number of students with better learning media is less than the number of students with poor learning media. Finally, the results presented here show that whether in an economically developed or an economically undeveloped region, the number of students who are enriched with learning media available is an important factor that affects online learning behavior and learning outcomes.","PeriodicalId":71434,"journal":{"name":"数据分析和信息处理(英文)","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70997814","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Competitive Advantage: A Study of Saudi SMEs to Adopt Data Mining for Effective Decision Making 竞争优势:沙特中小企业采用数据挖掘进行有效决策的研究

数据分析和信息处理(英文) Pub Date : 2022-01-01 DOI: 10.4236/jdaip.2022.103010

Tariq Saeed Mian, F. Ghabban

引用次数: 1

Detection and Selection of Moving Objects in Video Images Based on Impulse and Recurrent Neural Networks 基于脉冲和递归神经网络的视频图像运动目标检测与选择

数据分析和信息处理(英文) Pub Date : 2022-01-01 DOI: 10.4236/jdaip.2022.102008

Ihar Yeuseyenka, Ihar Melnikau, I. Yemelyanov

引用次数: 1

A Short Review of Classification Algorithms Accuracy for Data Prediction in Data Mining Applications 浅谈数据挖掘应用中数据预测分类算法的准确性

数据分析和信息处理(英文) Pub Date : 2021-07-08 DOI: 10.4236/jdaip.2021.93011

Ibrahim Ba’abbad, Thamer Althubiti, Abdulmohsen Alharbi, Khalid Alfarsi, S. Rasheed

{"title":"A Short Review of Classification Algorithms Accuracy for Data Prediction in Data Mining Applications","authors":"Ibrahim Ba’abbad, Thamer Althubiti, Abdulmohsen Alharbi, Khalid Alfarsi, S. Rasheed","doi":"10.4236/jdaip.2021.93011","DOIUrl":"https://doi.org/10.4236/jdaip.2021.93011","url":null,"abstract":"Many business applications rely on their historical data to predict their business future. The marketing products process is one of the core processes for the business. Customer needs give a useful piece of information that helps to market the appropriate products at the appropriate time. Moreover, services are considered recently as products. The development of education and health services is depending on historical data. For the more, reducing online social media networks problems and crimes need a significant source of information. Data analysts need to use an efficient classification algorithm to predict the future of such businesses. However, dealing with a huge quantity of data requires great time to process. Data mining involves many useful techniques that are used to predict statistical data in a variety of business applications. The classification technique is one of the most widely used with a variety of algorithms. In this paper, various classification algorithms are revised in terms of accuracy in different areas of data mining applications. A comprehensive analysis is made after delegated reading of 20 papers in the literature. This paper aims to help data analysts to choose the most suitable classification algorithm for different business applications including business in general, online social media networks, agriculture, health, and education. Results show FFBPN is the most accurate algorithm in the business domain. The Random Forest algorithm is the most accurate in classifying online social networks (OSN) activities. Naïve Bayes algorithm is the most accurate to classify agriculture datasets. OneR is the most accurate algorithm to classify instances within the health domain. The C4.5 Decision Tree algorithm is the most accurate to classify students’ records to predict degree completion time.","PeriodicalId":71434,"journal":{"name":"数据分析和信息处理(英文)","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46038670","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

A Panel Data Analysis of the Impact of Chinese Foreign Direct Investment (FDI), Remittances and Foreign Aid on Human Capital Growth and Brain Drain in Africa 中国对外直接投资、汇款和对外援助对非洲人力资本增长和人才流失影响的面板数据分析

数据分析和信息处理(英文) Pub Date : 2021-07-08 DOI: 10.4236/jdaip.2021.93012

O. T. Tasinda, Tian Ze, S. A. Imanche

{"title":"A Panel Data Analysis of the Impact of Chinese Foreign Direct Investment (FDI), Remittances and Foreign Aid on Human Capital Growth and Brain Drain in Africa","authors":"O. T. Tasinda, Tian Ze, S. A. Imanche","doi":"10.4236/jdaip.2021.93012","DOIUrl":"https://doi.org/10.4236/jdaip.2021.93012","url":null,"abstract":"The main purpose of this research was to analyze the impact the Chinese foreign direct \u0000investment (FDI), remittances, and foreign aid have had to human capital growth \u0000(HCG) and brain drain. The study data were collected from five African countries (Nigeria, Kenya, Ghana, South \u0000Africa, and Morocco) from 2009 to 2018. Secondary sources were used in data \u0000collection, then autoregressive distributed lag (ARDL) modeling was used in the \u0000analysis. Before modelling was done, co-integration tests and panel unit were \u0000applied. The results revealed that Chinese FDI, remittances, and foreign aid \u0000had a significant and positive impact on HCG in the long but not the short-run. \u0000Besides, remittances, Chinese FDI, and foreign aid demonstrated significant \u0000negative impacts on brain drain in the long term, not in the short term. This \u0000study makes important practical and theoretical contributions about the roles \u0000of Chinese FDI, remittances, and foreign aid in the reduction of brain drain \u0000and the growth of human capital.","PeriodicalId":71434,"journal":{"name":"数据分析和信息处理(英文)","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41368597","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Classification of Acupuncture Points Based on the Bert Model* 基于Bert模型的穴位分类*

数据分析和信息处理(英文) Pub Date : 2021-07-08 DOI: 10.4236/jdaip.2021.93008

Xiong Zhong, Yangli Jia, Dekui Li, Xiangliang Zhang

引用次数: 2